Have you ever dreamt of building robots that twirl waltzes, crafting algorithms that write sonnets, or conjuring computer programs that outwit the wiliest fox? That’s where Johns Hopkins University comes in. With the expertise of the Johns Hopkins Applied Physics Laboratory (APL), the university offers one of the nation’s first online Master’s programs in Artificial Intelligence—designed specifically for engineers who want to step into this exciting, fast-moving field.
The curriculum is intended to provide a thorough overview of the core fields of AI—robotics, natural language processing, image processing, and others—while also allowing you to tailor your studies to your interests and career goals. Whether you’re working on AI features, cutting-edge robotics, or studying how machines read and process language, this program combines theory with practical, real-world applications.
One of this program’s unique aspects is its leadership team, which is made up of top-tier researchers, scientists, and engineers at the forefront of AI innovation. They don’t just teach the material; they live it, ensuring that you get the most current, relevant knowledge available. And because it’s fully online, you can pursue this degree on your own schedule, without having to put your career on hold.
Master’s Program Focus: Building expertise across Core Artificial Intelligence Domains
The Master’s in Artificial Intelligence at Johns Hopkins University is designed to provide you with a robust, interdisciplinary foundation in AI, blending theoretical insights with hands-on practical applications. The program’s focus is to equip students with the skills necessary to tackle complex AI problems, from designing intelligent systems to applying machine learning algorithms in real-world scenarios.
The curriculum is structured to build deep expertise in several critical areas of AI, including algorithms, machine learning, robotics, natural language processing, and the creation of AI-driven systems. Core courses lay the groundwork, while a wide selection of electives allows you to explore specialized topics and tailor your education to your career aspirations. Whether you’re focused on AI theory, application, or creating integrated AI systems, the program provides the flexibility and depth to meet your goals.
Core Courses Overview
- Introduction to Algorithms: This course focuses on the design and analysis of algorithms, covering topics like dynamic programming, sorting, searching, graph algorithms, and algorithmic complexity. A solid understanding of these concepts is crucial for all areas of AI, from data science to robotics.
- Algorithms for Data Science:Algorithms play a vital role in data science and AI, and this course provides an in-depth exploration of algorithmic concepts tailored for data problems. You’ll cover essential topics like data preprocessing, optimization algorithms, statistical algorithms, and feature transformation techniques. This course prepares you to work with large, complex datasets and apply AI models to real-world data.
- Artificial Intelligence: A foundational course in AI, this class introduces core concepts such as reasoning, optimization, and pattern recognition. It explores both symbolic AI (logic-based systems) and machine learning techniques, giving you a broad understanding of AI’s hybrid nature. You’ll learn about state space search, Bayesian networks, expert systems, reinforcement learning, and neural networks, all through practical assignments in Python.
- Applied Machine Learning: This course focuses on the practical application of machine learning techniques to real-world problems. You’ll explore different machine learning approaches, including anomaly detection, deep learning, and ensemble learning. Using tools like Python-based Anaconda and Jupyter Notebooks, you’ll work with real datasets to solve challenges in areas like image recognition, medical diagnosis, and predictive analytics.
- Creating AI-Enabled Systems: AI isn’t just about models—it’s about creating systems that effectively use algorithms, data, and computing power to deliver intelligent solutions. This course takes a systems-level approach to AI, guiding you through the lifecycle of building AI-enabled systems. You’ll learn how to decompose complex problems, design AI architectures, and implement solutions in areas like computer vision, natural language processing, and robotics.
List of Electives (must choose minimum six)
The Master’s in Artificial Intelligence program at Johns Hopkins offers a broad selection of electives. Students must take at least six of the following courses:
- UAV Systems and Control;
- Machine Learning for Signal Processing;
- Introduction to Pattern Recognition;
- Deep Learning for Computer Vision;
- Intelligent Algorithms;
- Human-Robotics Interaction;Introduction to Robotics;
- Introduction to GPU Programming;
- Logic: Systems, Semantics, and Models;
- Social Media Analytics;
- Crowdsourcing and Human Computation;
- Cloud Computing;Natural Language Processing; and, endlessly, several more…
Admission Requirements for the Artificial Intelligence Master’s at Johns Hopkins University
So, you’re ready to take on the Artificial Intelligence Master’s at Johns Hopkins? Fantastic! Here’s what you’ll need to join this journey:
What You Need to Get In
To apply for the Master’s in Artificial Intelligence program, you’ll need to meet the general admission requirements for all master’s candidates, plus a few prerequisites to make sure you’re prepared for the advanced AI coursework ahead. Here’s what you’ll need under your belt before you apply:
- Calculus (3 semesters or 5 quarters)
- Think of this as your AI toolkit. We require foundational courses in calculus, including multivariable calculus, so you’re ready for the math-heavy side of AI.
- Linear Algebra (1 semester)
- A solid understanding of linear algebra is crucial for AI. It’s the math behind machine learning algorithms and data transformations.
- Probability & Statistics (1 semester)
- AI loves data, and data loves probability. You’ll need a solid foundation in statistics to make sense of the numbers.
- Programming (1 semester of Java or Python)
- Coding is your AI magic wand. Whether you prefer Python’s simplicity or Java’s structure, this skill is essential for tackling real-world problems.
- Advanced Programming/Data Structures (1 semester)
- Deepen your coding prowess with courses in data structures—because AI requires more than just basic programming.
If you’ve got gaps in your prerequisites, no worries! You can still apply with provisional status while completing these courses. You can take them through Johns Hopkins Engineering or at another accredited institution.
Degree Requirements: How to get your Master’s degree in 3-5 years
Once you’re in, the real fun begins. You’ll need to complete ten courses within five years, including:
- 4 Core Courses that cover the essentials of AI.
- 6 Elective Courses that let you tailor the program to your interests.
- You’ll also need to take 3 courses at the 700-level.
- If you’ve already completed graduate-level coursework in equivalent subjects, you may be able to waive certain core courses, making room for more electives. (Yes, it’s as awesome as it sounds.)
And just to keep things interesting, there are two foundation tracks to choose from: applied or theoretical. Each gives you a slightly different spin on your AI journey, so pick the one that suits you best.
You can complete this course in 3 years or less if you pace yourself
Understanding Tuition and Financial Support
Pursuing a Master’s in Artificial Intelligence at Johns Hopkins is an exciting step toward advancing your career in one of today’s most innovative fields. However, as with any valuable educational experience, it’s important to understand the associated costs—and how to make those costs work for you. Here’s a closer look at tuition, fees, and financial support available to students in the Engineering for Professionals (EP) program.
Tuition Fees for the Master’s in AI : Investing in Your Future
For students enrolled in graduate-level courses in the Johns Hopkins Whiting School of Engineering, the standard tuition is $6,470 per three-credit course. But here’s the good news: thanks to the Dean’s Fellowship, which automatically applies to all EP students, the cost per course is significantly reduced. After the fellowship is applied, the tuition drops to $5,270 per course, making the overall investment in your education more manageable.
Here’s a quick breakdown of tuition costs for the 2024–2025 academic year:
- Graduate Course Tuition (before fellowship): $6,470 per course
- Dean’s Fellowship (automatically applied): -$1,200 per course
- Final Course Tuition (after fellowship): $5,270 per course
If you complete all ten required courses during the 2024–2025 academic year, the total cost of the program will be approximately $52,700. While that’s a significant investment, the Dean’s Fellowship helps alleviate some of the financial burden, making this world-class education more accessible.
Employer Contributions for Educational Aid: A Potential Financial Boost
A pleasant surprise for many students is that 78% of enrolled students have their tuition covered by employer contribution programs. If your employer offers tuition assistance, this can greatly reduce your out-of-pocket costs. But it’s important to plan ahead. You’ll need to coordinate with your employer well before registration deadlines to ensure your tuition payments are processed in time for course enrollment.
Do note that you are ultimately responsible for confirming that payment has been made. If your employer is covering tuition, you won’t be able to register for classes until that payment is confirmed.
If you’re in a position where your employer covers the cost, this can be an excellent way to make your education even more affordable.
The Dean’s Fellowship: A Helping Hand
Every student in the EP program automatically benefits from the Dean’s Fellowship. This fellowship provides a $1,200 reduction in tuition per course, lowering the cost from $6,470 to $5,270 per three-credit graduate course. This financial support is one of the ways Johns Hopkins makes high-quality education more accessible, helping you focus on your studies rather than worrying about tuition.
With the Dean’s Fellowship applied, your total cost per course is reduced, making your path toward earning a Master’s in AI both more affordable and manageable. It’s one of the many ways the program is designed to help you succeed.
Johns Hopkins AI Master’s: The Good, The Bad, and The Surprising!
As with any academic journey, the Master’s in Artificial Intelligence at Johns Hopkins University has its strengths and areas where it might not be the perfect fit for every prospective student. Here’s a breakdown of what we appreciated about the program and some things that might cause potential students to pause.
What We Liked About the Program
- Comprehensive Curriculum with a Balance of Theory and Practice
The program stands out for its ability to blend theoretical foundations with hands-on practical experience. From the “Introduction to Algorithms” course, which lays a strong foundation in algorithmic design, to “Applied Machine Learning,” where students get to work with real-world datasets, the curriculum ensures students get both the deep, conceptual knowledge and practical skills they need to excel in AI. This makes graduates well-rounded and ready for complex challenges in both academic and industrial settings. - Wide Range of Electives
One of the major benefits of the program is the vast number of electives that students can choose from. Whether you’re interested in deep learning, computer vision, UAV systems, or natural language processing, there’s plenty of room to specialize and cater the education to your specific career aspirations. - Financial Support via the Dean’s Fellowship
The automatic $1,200 reduction per course through the Dean’s Fellowship is a fantastic benefit that makes this world-class program more accessible. With the cost of graduate education continually on the rise, this financial support helps ease the burden, allowing students to focus more on their studies and less on tuition bills. - Employer Contribution Programs
It’s also worth mentioning that a significant percentage of students (78%) benefit from employer tuition assistance programs. If your company offers educational support, this can greatly reduce the overall cost of the program. For students with supportive employers, this can make the program even more affordable, adding a financial layer of flexibility.
What We Didn’t Like About the Program
- Heavy Mathematical Prerequisites
While the program offers a thorough education in AI, it does require a strong background in mathematics—specifically multivariable calculus, linear algebra, and probability/statistics. For those without a solid foundation in these areas, it could feel a bit daunting at first. While missing prerequisites can be made up through provisional admission, students must complete these courses before progressing to more advanced work, which could delay graduation or add additional coursework to an already packed schedule. - Provisional Admission Process
The option for provisional admission for students who don’t meet all the prerequisites may be helpful, but it can also feel like an extra hurdle. While you can complete missing prerequisites before fully entering the program, this additional step may not appeal to students who want to start their advanced coursework immediately. For those who are more academically prepared, this could be a detour that delays their start in the core program. - Rigorous Coursework Load
The program requires ten courses to be completed within five years. While this is manageable for most students, it can be intense, especially if you’re juggling work and other responsibilities. With core and elective courses to balance, the program can quickly become overwhelming if you’re not able to manage your time effectively. Some students may find the workload challenging, especially if they’re trying to work full-time while completing their coursework.
Our Overall Review
The Master’s in Artificial Intelligence at Johns Hopkins is a solid program that mixes theory with real-world experience. We liked the strong core courses that cover key AI topics like algorithms, machine learning, and building AI systems. Plus, the variety of electives gives students the chance to tailor their learning to their career goals. The program is designed for working professionals, so the pace is manageable, and you can choose between more practical or theoretical tracks. While the tuition is high, the Dean’s Fellowship and employer funding options make it more affordable. Overall, it’s a great choice for anyone looking to advance their career in AI.
Prestige vs. Flexibility: Johns Hopkins vs. UPenn Artificial Intelligence Master’s Showdown
When comparing the AI Master’s programs at Johns Hopkins and UPenn, both offer prestigious, flexible online options but with some key differences. Johns Hopkins offers a highly flexible curriculum with the choice between applied or theoretical tracks and a wide range of electives, making it ideal for those seeking customization. In contrast, UPenn follows a more structured approach with 7 core courses and fewer elective choices but boasts its Ivy League pedigree and a unique AI Practicum course for hands-on experience. While both programs are costly, Johns Hopkins provides a Dean’s Fellowship and strong employer contribution options, whereas UPenn offers federal financial aid and a competitive Early Bird scholarship. The admissions process at both institutions emphasizes professional experience, though Johns Hopkins also allows provisional status for students who need to complete prerequisites. Ultimately, if you’re looking for flexibility and broad specialization, Johns Hopkins may be the better fit, while UPenn is a strong choice for those seeking the prestige and networking opportunities of an Ivy League degree.
John Hopkins vs. Georgia Tech: Which to choose between these AI Titans?
When it comes to AI, two titans are battling for your attention: Georgia Tech and Johns Hopkins. On one side, Georgia Tech’s OMSCS is the affordable, flexible powerhouse, offering three AI tracks that let you learn from world-class faculty, build robots, and dive into hands-on projects—all without draining your wallet. On the other, Johns Hopkins flexes its Ivy League prestige with a deep, demanding AI Master’s program that combines cutting-edge theory with real-world applications. But beware: the price tag is hefty, and math prerequisites are no joke. Whether you’re all about Georgia Tech’s budget-friendly flexibility or Hopkins’ elite, rigorous curriculum, both programs will catapult you into the AI stratosphere.
Johns Hopkins’ Elite Curriculum vs. UT Austin’s Affordable Excellence
When it comes to AI Master’s programs, Johns Hopkins and UT Austin each offer distinct paths. Hopkins stands out for its rigorous blend of theory and application, with a broad curriculum covering everything from machine learning to robotics. The program is well-respected but comes with a higher price tag and requires solid math foundations. On the other hand, UT Austin’s online AI Master’s offers an affordable, flexible option at $10,000 for the full program. With asynchronous courses and a mix of core and elective topics like deep learning and ethics in AI, it provides solid industry relevance. While Hopkins offers a prestigious, in-depth experience, Austin’s program is a cost-effective way to gain solid AI expertise with flexibility.
Want to look at more colleges? Check out CollegeHippo’s archives for Online master’s in Artificial Intelligence at some of the top universities at graduate levels.
References
- https://ep.jhu.edu/programs/artificial-intelligence/
- https://ep.jhu.edu/admissions-aid/tuition-fees/
- https://ep.jhu.edu/programs/artificial-intelligence/masters-degree-requirements/